Nanochannel Highways in Hybrid Lamellar Membranes: Computational Simulation of Electric Field-Guided CO<sub>2</sub> Transport via Molecular Sieving for Ultra-efficient Separation
Bibliographic record
Abstract
The excessive weakness and strength of the interactions between graphene and g-C 3 N 4 with CO 2 pose challenges for CO 2 separation. Here, we proposed a gas separation nanochannel composed of the interlayer spacing in a two-dimensional graphene/g-C 3 N 4 (Gra/CN) membrane to solve the issue by molecular dynamics simulation. Graphene is a finely tuned electrostatic interaction membrane in direct contact with CO 2 within the nanochannel. Due to the proper interaction between Gra/CN and CO 2, Gra/CN maintains high CO 2 permeance and selectivity under mixed gas conditions at different interlayer spacings, which confirms the good applicability for CO 2 separation. The nanochannel becomes a highway for CO 2 separation under an external electric field ( E field ) of 1.0 × 10 –4 V·Å –1 along the z -axis; the CO 2 permeance reaches 1.17 × 10 –3 mol·s –1 ·m –2 ·Pa –1 through computational simulation, marking a substantial enhancement of approximately 60.3% relative to conditions without E field . Simultaneously, the solubility coefficient rises to 4.48 × 10 7 mol·m –4 ·Pa as E field in the z -axis. Moreover, the calculated energy consumption of the CO 2 separation is 0.017 GJ·ton –1, which is below the theoretical minimum value of 0.050 GJ·ton –1, demonstrating practical feasibility and efficiency in real-world applications. The results of this work highlight the significant role of the synergistic effect of the hybrid membrane gas separation nanochannel and E field in enhancing CO 2 solubility and permeance, providing valuable theoretical guidance for CO 2 separation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".